tensorflow微调模型，如何中断梯度

• import tensorflow as tf
• # Load the VGG-16 model in the default graph
• vgg_saver = tf.train.import_meta_graph(dir + '/vgg/results/vgg-16.meta')
• # Access the graph
• vgg_graph = tf.get_default_graph()
• # Retrieve VGG inputs
  self.x_plh = vgg_graph.get_tensor_by_name('input:0')
  # Choose which node you want to connect your own graph
  output_conv =vgg_graph.get_tensor_by_name('conv1_2:0')
  # output_conv =vgg_graph.get_tensor_by_name('conv2_2:0')
  # output_conv =vgg_graph.get_tensor_by_name('conv3_3:0')
• # output_conv =vgg_graph.get_tensor_by_name('conv4_3:0')
• # output_conv =vgg_graph.get_tensor_by_name('conv5_3:0')
• # Stop the gradient for fine-tuning
• output_conv_sg = tf.stop_gradient(output_conv) # It's an identity functio
• # Build further operations
• output_conv_shape = output_conv_sg.get_shape().as_list()
• W1 = tf.get_variable('W1', shape=[1, 1, output_conv_shape[3], 32], initializer=tf.random_normal_initializer(stddev=1e-1))
• b1 = tf.get_variable('b1', shape=[32], initializer=tf.constant_initializer(0.1))
• z1 = tf.nn.conv2d(output_conv_sg, W1, strides=[1, 1, 1, 1], padding='SAME') + b1
• a = tf.nn.relu(z1)